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2017 | OriginalPaper | Chapter

Improving Serendipity and Accuracy in Cross-Domain Recommender Systems

Authors : Denis Kotkov, Shuaiqiang Wang, Jari Veijalainen

Published in: Web Information Systems and Technologies

Publisher: Springer International Publishing

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Abstract

Cross-domain recommender systems use information from source domains to improve recommendations in a target domain, where the term domain refers to a set of items that share attributes and/or user ratings. Most works on this topic focus on accuracy but disregard other properties of recommender systems. In this paper, we attempt to improve serendipity and accuracy in the target domain with datasets from source domains. Due to the lack of publicly available datasets, we collect datasets from two domains related to music, involving user ratings and item attributes. We then conduct experiments using collaborative filtering and content-based filtering approaches for the purpose of validation. According to our results, the source domain can improve serendipity in the target domain for both approaches. The source domain decreases accuracy for content-based filtering and increases accuracy for collaborative filtering. The improvement of accuracy decreases with the growth of non-overlapping items in different domains.

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Footnotes
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Metadata
Title
Improving Serendipity and Accuracy in Cross-Domain Recommender Systems
Authors
Denis Kotkov
Shuaiqiang Wang
Jari Veijalainen
Copyright Year
2017
Book
Web Information Systems and Technologies

Print ISBN: 978-3-319-66467-5

Electronic ISBN: 978-3-319-66468-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-66468-2_6

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